Chapter 11
Managing
Knowledge and
Collaboration
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Management Information Systems
Chapter 11 Managing Knowledge
LEARNING OBJECTIVES
• Assess the role of knowledge management and
knowledge management programs in business.
• Describe the types of systems used for enterprisewide knowledge management and demonstrate how
they provide value for organizations.
• Describe the major types of knowledge work systems
and assess how they provide value for firms.
• Evaluate the business benefits of using intelligent
techniques for knowledge management.
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Management Information Systems
Chapter 11 Managing Knowledge
The Knowledge Management Landscape
• Sales of enterprise content management software for
knowledge management expected to grow 15 percent
annually through 2012
• Information Economy
• 55% U.S. labor force: knowledge and information workers
• 60% U.S. GDP from knowledge and information sectors
• Substantial part of a firm’s stock market value is related to
intangible assets: knowledge, brands, reputations, and
unique business processes
• Knowledge-based projects can produce extraordinary ROI
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Management Information Systems
Chapter 11 Managing Knowledge
The Knowledge Management Landscape
U.S. Enterprise Knowledge Management
Software Revenues, 2005-2012
Figure 11-1
Enterprise knowledge
management software
includes sales of content
management and portal
licenses, which have been
growing at a rate of 15
percent annually, making it
among the fastest-growing
software applications.
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Management Information Systems
Chapter 11 Managing Knowledge
The Knowledge Management Landscape
• Important dimensions of knowledge
• Knowledge is a firm asset
• Intangible
• Creation of knowledge from data, information, requires
organizational resources
• As it is shared, experiences network effects
• Knowledge has different forms
•
•
•
•
11.5
May be explicit (documented) or tacit (residing in minds)
Know-how, craft, skill
How to follow procedure
Knowing why things happen (causality)
© 2010 by Prentice Hall
Management Information Systems
Chapter 11 Managing Knowledge
The Knowledge Management Landscape
• Important dimensions of knowledge (cont.)
• Knowledge has a location
• Cognitive event
• Both social and individual
• “Sticky” (hard to move), situated (enmeshed in firm’s culture),
contextual (works only in certain situations)
• Knowledge is situational
• Conditional: Knowing when to apply procedure
• Contextual: Knowing circumstances to use certain tool
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Management Information Systems
Chapter 11 Managing Knowledge
The Knowledge Management Landscape
• To transform information into knowledge, firm must expend
additional resources to discover patterns, rules, and contexts where
knowledge works
• Wisdom: Collective and individual experience of applying
knowledge to solve problems
• Involves where, when, and how to apply knowledge
• Knowing how to do things effectively and efficiently in ways other
organizations cannot duplicate is primary source of profit and
competitive advantage that cannot be purchased easily by
competitors
• E.g., Having a unique build-to-order production system
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Management Information Systems
Chapter 11 Managing Knowledge
The Knowledge Management Landscape
• Organizational learning
• Process in which organizations learn
• Gain experience through collection of data,
measurement, trial and error, and feedback
• Adjust behavior to reflect experience
• Create new business processes
• Change patterns of management decision making
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Management Information Systems
Chapter 11 Managing Knowledge
The Knowledge Management Landscape
• Knowledge management: Set of business processes
developed in an organization to create, store, transfer,
and apply knowledge
• Knowledge management value chain:
• Each stage adds value to raw data and information
as they are transformed into usable knowledge
• Knowledge acquisition
• Knowledge storage
• Knowledge dissemination
• Knowledge application
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Management Information Systems
Chapter 11 Managing Knowledge
The Knowledge Management Landscape
• Knowledge management value chain
• Knowledge acquisition
• Documenting tacit and explicit knowledge
• Storing documents, reports, presentations, best practices
• Unstructured documents (e.g., e-mails)
• Developing online expert networks
• Creating knowledge
• Tracking data from TPS and external sources
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Management Information Systems
Chapter 11 Managing Knowledge
The Knowledge Management Landscape
• Knowledge management value chain:
• Knowledge storage
• Databases
• Document management systems
• Role of management:
• Support development of planned knowledge storage
systems
• Encourage development of corporate-wide schemas
for indexing documents
• Reward employees for taking time to update and
store documents properly
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Management Information Systems
Chapter 11 Managing Knowledge
The Knowledge Management Landscape
• Knowledge management value chain:
• Knowledge dissemination
• Portals
• Push e-mail reports
• Search engines
• Collaboration tools
• A deluge of information?
• Training programs, informal networks, and shared
management experience help managers focus
attention on important information
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Management Information Systems
Chapter 11 Managing Knowledge
The Knowledge Management Landscape
• Knowledge management value chain:
• Knowledge application
• To provide return on investment, organizational
knowledge must become systematic part of
management decision making and become situated in
decision-support systems
• New business practices
• New products and services
• New markets
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Management Information Systems
Chapter 11 Managing Knowledge
The Knowledge Management Landscape
The Knowledge Management Value Chain
Figure 11-2
Knowledge management
today involves both
information systems
activities and a host of
enabling management and
organizational activities.
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Management Information Systems
Chapter 11 Managing Knowledge
The Knowledge Management Landscape
• New organizational roles and responsibilities
• Chief knowledge officer executives
• Dedicated staff / knowledge managers
• Communities of practice (COPs)
• Informal social networks of professionals and employees
within and outside firm who have similar work-related
activities and interests
• Activities include education, online newsletters, sharing
experiences and techniques
• Facilitate reuse of knowledge, discussion
• Reduce learning curves of new employees
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Management Information Systems
Chapter 11 Managing Knowledge
The Knowledge Management Landscape
• Three major types of knowledge management
systems:
• Enterprise-wide knowledge management systems
• General-purpose firm-wide efforts to collect, store, distribute, and
apply digital content and knowledge
• Knowledge work systems (KWS)
• Specialized systems built for engineers, scientists, other knowledge
workers charged with discovering and creating new knowledge
• Intelligent techniques
• Diverse group of techniques such as data mining used for various
goals: discovering knowledge, distilling knowledge, discovering
optimal solutions
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Management Information Systems
Chapter 11 Managing Knowledge
The Knowledge Management Landscape
Major Types of Knowledge Management Systems
There are three major categories of knowledge management systems, and each can be
broken down further into more specialized types of knowledge management systems.
Figure 11-3
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Management Information Systems
Chapter 11 Managing Knowledge
Enterprise-Wide Knowledge Management Systems
• Three major types of knowledge in enterprise
• Structured documents
• Reports, presentations
• Formal rules
• Semistructured documents
• E-mails, videos
• Unstructured, tacit knowledge
• 80% of an organization’s business content is
semistructured or unstructured
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Management Information Systems
Chapter 11 Managing Knowledge
Enterprise-Wide Knowledge Management Systems
• Enterprise-wide content management
systems
• Help capture, store, retrieve, distribute, preserve
• Documents, reports, best practices
• Semistructured knowledge (e-mails)
• Bring in external sources
• News feeds, research
• Tools for communication and collaboration
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Management Information Systems
Chapter 11 Managing Knowledge
Enterprise-Wide Knowledge Management Systems
An Enterprise Content Management System
An enterprise content management system has capabilities for classifying, organizing, and
managing structured and semistructured knowledge and making it available throughout the
enterprise
Figure 11-4
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Management Information Systems
Chapter 11 Managing Knowledge
Enterprise-Wide Knowledge Management Systems
• Enterprise-wide content management
systems
• Key problem – Developing taxonomy
• Knowledge objects must be tagged with categories for
retrieval
• Digital asset management systems
• Specialized content management systems for classifying,
storing, managing unstructured digital data
• Photographs, graphics, video, audio
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Management Information Systems
Chapter 11 Managing Knowledge
Enterprise-Wide Knowledge Management Systems
• Knowledge network systems
• Provide online directory of corporate experts in well-defined
knowledge domains
• Use communication technologies to make it easy for employees
to find appropriate expert in a company
• May systematize solutions developed by experts and store them
in knowledge database
• Best-practices
• Frequently asked questions (FAQ) repository
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Management Information Systems
Chapter 11 Managing Knowledge
Enterprise-Wide Knowledge Management Systems
An Enterprise Knowledge Network System
Figure 11-5
A knowledge network maintains a
database of firm experts, as well as
accepted solutions to known
problems, and then facilitates the
communication between employees
looking for knowledge and experts
who have that knowledge. Solutions
created in this communication are
then added to a database of
solutions in the form of FAQs, best
practices, or other documents.
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Management Information Systems
Chapter 11 Managing Knowledge
Enterprise-Wide Knowledge Management Systems
• Major knowledge management system vendors
include powerful portal and collaboration technologies
• Portal technologies: Access to external information
• News feeds, research
• Access to internal knowledge resources
• Collaboration tools
• E-mail
• Discussion groups
• Blogs
• Wikis
• Social bookmarking
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Management Information Systems
Chapter 11 Managing Knowledge
Enterprise-Wide Knowledge Management Systems
• Learning management systems
• Provide tools for management, delivery, tracking, and
assessment of various types of employee learning and
training
• Support multiple modes of learning
• CD-ROM, Web-based classes, online forums, live instruction,
etc.
• Automates selection and administration of courses
• Assembles and delivers learning content
• Measures learning effectiveness
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Management Information Systems
Chapter 11 Managing Knowledge
Enterprise-Wide Knowledge Management Systems
Managing with Web 2.0
• Read the Interactive Session: Management, and then
discuss the following questions:
• How do Web 2.0 tools help companies manage knowledge,
coordinate work, and enhance decision making?
• What business problems do blogs, wikis, and other social
networking tools help solve?
• Describe how a company such as Wal-Mart or Proctor &
Gamble would benefit from using Web 2.0 tools internally.
• What challenges do companies face in spreading the use of
Web 2.0? What issues should managers be concerned with?
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Management Information Systems
Chapter 11 Managing Knowledge
Knowledge Work Systems
• Knowledge work systems
• Systems for knowledge workers to help create new knowledge
and ensure that knowledge is properly integrated into business
• Knowledge workers
• Researchers, designers, architects, scientists, and engineers
who create knowledge and information for the organization
• Three key roles:
• Keeping organization current in knowledge
• Serving as internal consultants regarding their areas of expertise
• Acting as change agents, evaluating, initiating, and promoting
change projects
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Management Information Systems
Chapter 11 Managing Knowledge
Knowledge Work Systems
• Requirements of knowledge work systems
• Substantial computing power for graphics, complex calculations
• Powerful graphics, and analytical tools
• Communications and document management capabilities
• Access to external databases
• User-friendly interfaces
• Optimized for tasks to be performed (design engineering,
financial analysis)
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Management Information Systems
Chapter 11 Managing Knowledge
Knowledge Work Systems
Requirements of Knowledge Work Systems
Knowledge work systems require strong links to external knowledge bases in addition to specialized hardware and software.
Figure 11-6
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Management Information Systems
Chapter 11 Managing Knowledge
Knowledge Work Systems
• Examples of knowledge work systems
• CAD (computer-aided design): Automates creation and
revision of engineering or architectural designs, using computers
and sophisticated graphics software
• Virtual reality systems: Software and special hardware to
simulate real-life environments
• E.g. 3-D medical modeling for surgeons
• VRML: Specifications for interactive, 3D modeling over Internet
• Investment workstations: Streamline investment process and
consolidate internal, external data for brokers, traders, portfolio
managers
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Management Information Systems
Chapter 11 Managing Knowledge
Intelligent Techniques
• Intelligent techniques: Used to capture individual and
collective knowledge and to extend knowledge base
• To capture tacit knowledge: Expert systems, case-based
reasoning, fuzzy logic
• Knowledge discovery: Neural networks and data mining
• Generating solutions to complex problems: Genetic
algorithms
• Automating tasks: Intelligent agents
• Artificial intelligence (AI) technology:
• Computer-based systems that emulate human behavior
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Management Information Systems
Chapter 11 Managing Knowledge
Intelligent Techniques
• Expert systems:
• Capture tacit knowledge in very specific and limited
domain of human expertise
• Capture knowledge of skilled employees as set of
rules in software system that can be used by others in
organization
• Typically perform limited tasks that may take a few
minutes or hours, e.g.:
• Diagnosing malfunctioning machine
• Determining whether to grant credit for loan
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Management Information Systems
Chapter 11 Managing Knowledge
Intelligent Techniques
Rules in an Expert System
Figure 11-7
An expert system
contains a number of
rules to be followed. The
rules are interconnected;
the number of outcomes
is known in advance and
is limited; there are
multiple paths to the
same outcome; and the
system can consider
multiple rules at a single
time. The rules illustrated
are for simple creditgranting expert systems.
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Management Information Systems
Chapter 11 Managing Knowledge
Intelligent Techniques
• How expert systems work
• Knowledge base: Set of hundreds or thousands of
rules
• Inference engine: Strategy used to search
knowledge base
• Forward chaining: Inference engine begins with information
entered by user and searches knowledge base to arrive at
conclusion
• Backward chaining: Begins with hypothesis and asks user
questions until hypothesis is confirmed or disproved
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Management Information Systems
Chapter 11 Managing Knowledge
Intelligent Techniques
Inference Engines in Expert Systems
An inference engine works by searching through the rules and “firing” those rules that are triggered by facts gathered and entered by the user.
A collection of rules is similar to a series of nested IF statements in a traditional software system; however the magnitude of the statements
and degree of nesting are much greater in an expert system
Figure 11-8
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Management Information Systems
Chapter 11 Managing Knowledge
Intelligent Techniques
• Successful expert systems
• Countrywide Funding Corporation in Pasadena, California, uses
expert system to improve decisions about granting loans
• Con-Way Transportation built expert system to automate and
optimize planning of overnight shipment routes for nationwide
freight-trucking business
• Most expert systems deal with problems of classification
• Have relatively few alternative outcomes
• Possible outcomes are known in advance
• Many expert systems require large, lengthy, and expensive
development and maintenance efforts
• Hiring or training more experts may be less expensive
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Management Information Systems
Chapter 11 Managing Knowledge
Intelligent Techniques
• Case-based reasoning (CBR)
• Descriptions of past experiences of human specialists,
represented as cases, stored in knowledge base
• System searches for stored cases with problem characteristics
similar to new one, finds closest fit, and applies solutions of old
case to new case
• Successful and unsuccessful applications are grouped with case
• Stores organizational intelligence: Knowledge base is
continuously expanded and refined by users
• CBR found in
• Medical diagnostic systems
• Customer support
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Management Information Systems
Chapter 11 Managing Knowledge
Intelligent Techniques
How Case-Based Reasoning Works
Figure 11-9
Case-based reasoning represents
knowledge as a database of past cases
and their solutions. The system uses a
six-step process to generate solutions to
new problems encountered by the user.
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Management Information Systems
Chapter 11 Managing Knowledge
Intelligent Techniques
• Fuzzy logic systems
• Rule-based technology that represents imprecision used in
linguistic categories (e.g., “cold,” “cool”) that represent range of
values
• Describe a particular phenomenon or process linguistically and
then represent that description in a small number of flexible rules
• Provides solutions to problems requiring expertise that is difficult
to represent with IF-THEN rules
• Autofocus in cameras
• Detecting possible medical fraud
• Sendai’s subway system use of fuzzy logic controls to
accelerate smoothly
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Management Information Systems
Chapter 11 Managing Knowledge
Intelligent Techniques
Fuzzy Logic for Temperature Control
The membership functions for the input called temperature are in the logic of the thermostat to control the room temperature.
Membership functions help translate linguistic expressions such as warm into numbers that the computer can manipulate.
Figure 11-10
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Management Information Systems
Chapter 11 Managing Knowledge
Intelligent Techniques
• Neural networks
• Find patterns and relationships in massive amounts of data that
are too complicated for human to analyze
• “Learn” patterns by searching for relationships, building models,
and correcting over and over again model’s own mistakes
• Humans “train” network by feeding it data inputs for which outputs
are known, to help neural network learn solution by example
• Used in medicine, science, and business for problems in pattern
classification, prediction, financial analysis, and control and
optimization
• Machine learning: Related AI technology allowing computers to
learn by extracting information using computation and statistical
methods
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Management Information Systems
Chapter 11 Managing Knowledge
Intelligent Techniques
How a Neural Network Works
A neural network uses rules it “learns” from patterns in data to construct a hidden layer of logic. The hidden
layer then processes inputs, classifying them based on the experience of the model. In this example, the
neural network has been trained to distinguish between valid and fraudulent credit card purchases.
Figure 11-11
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Chapter 11 Managing Knowledge
Intelligent Techniques
Reality Mining
• Read the Interactive Session: Technology, and then
discuss the following questions:
• Why might businesses be interested in location-based mobile
networking?
• What technological developments have set the stage for the
growth of Sense Networks and the success of their products?
• Do you feel that the privacy risks surrounding CitySense are
significant? Would you sign up to use Sense Network
services? Why or why not?
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Management Information Systems
Chapter 11 Managing Knowledge
Intelligent Techniques
• Genetic algorithms
• Useful for finding optimal solution for specific problem by
examining very large number of possible solutions for that
problem
• Conceptually based on process of evolution
• Search among solution variables by changing and
reorganizing component parts using processes such as
inheritance, mutation, and selection
• Used in optimization problems (minimization of costs, efficient
scheduling, optimal jet engine design) in which hundreds or
thousands of variables exist
• Able to evaluate many solution alternatives quickly
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Management Information Systems
Chapter 11 Managing Knowledge
Intelligent Techniques
The Components of a Genetic Algorithm
This example illustrates an initial population of “chromosomes,” each representing a different solution. The genetic algorithm uses an iterative
process to refine the initial solutions so that the better ones, those with the higher fitness, are more likely to emerge as the best solution.
Figure 11-12
11.45
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Management Information Systems
Chapter 11 Managing Knowledge
Intelligent Techniques
• Hybrid AI systems
• Genetic algorithms, fuzzy logic, neural networks,
and expert systems integrated into single
application to take advantage of best features of
each
• E.g., Matsushita “neurofuzzy” washing machine
that combines fuzzy logic with neural networks
11.46
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Management Information Systems
Chapter 11 Managing Knowledge
Intelligent Techniques
• Intelligent agents
• Work in background to carry out specific, repetitive, and
predictable tasks for user, process, or software application
• Use limited built-in or learned knowledge base to accomplish
tasks or make decisions on user’s behalf
• Deleting junk e-mail
• Finding cheapest airfare
• Agent-based modeling applications:
• Systems of autonomous agents
• Model behavior of consumers, stock markets, and supply
chains; used to predict spread of epidemics
11.47
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